ArticlePLOS digital health2023
Digital Determinants of Health: Health data poverty amplifies existing health disparities-A scoping review.
Article in PLOS digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Recommended guidelines concerning artificial intelligence and health equity: a scoping review.Health affairs scholar · 2026Review
- Trust in Digital Sexual Health Information Among Chinese College Students: Qualitative Study.Journal of medical Internet research · 2026Article
- Online interest in calcitonin gene-related peptide-therapies: An infodemiology study using Google trends.Headache · 2026Article
- From Data Poverty to Data Sovereignty: Operationalizing Gold-Standard Biomedical Datasets in Low- and Middle-Income Countries.JMIR medical informatics · 2026Article
- Trust-Centered Design and Feasibility Evaluation of an AI-Enabled Conversational Health Tool for Sexual and Reproductive Health Among Rural Young Adults: Protocol for a Mixed Methods Study.JMIR research protocols · 2026Article
- Accessibility of Digital Financial Applications for People With Visual Impairment: Scoping Review.Journal of medical Internet research · 2026Article
- A global call to action on the digital determinants of health.NPJ digital medicine · 2026Review
- Disparities in Headache Care and Research in Children and Adolescents: A Narrative Review.Current pain and headache reports · 2026Review
- Using voice and speech data in healthcare: a scoping review of the ethical, legal and social implications.Frontiers in digital health · 2026Review
- Evaluating few-shot prompting for spectrogram-based lung sound classification using a multimodal language model.PLOS digital health · 2026Article
- Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities.Journal of clinical medicine · 2025Article
- CRISPR and Artificial Intelligence in Neuroregeneration: Closed-Loop Strategies for Precision Medicine, Spinal Cord Repair, and Adaptive Neuro-Oncology.International journal of molecular sciences · 2025Review
- How multi-scale modeling can help examine social determinants of health and resulting disparities.PLoS computational biology · 2025Article
- Digital determinants of sexual and reproductive health-workforce perspectives on digital and data literacies.Health promotion international · 2025Article
- A scoping review and expert consensus on digital determinants of health.Bulletin of the World Health Organization · 2025Article
- Digital divide as a determinant of health in the U.S. older adults: prevalence, trends, and risk factors.BMC geriatrics · 2024Article
- A scoping review of reporting gaps in FDA-approved AI medical devices.NPJ digital medicine · 2024Article
- Strengthening the evidence base on the use of digital health technologies to accelerate progress towards universal health coverage.Oxford open digital health · 2024Article
- Introducing the "AI Language Models in Health Care" Section: Actionable Strategies for Targeted and Wide-Scale Deployment.JMIR medical informatics · 2023Article
- Digital determinants of health: Editorial.PLOS digital health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.